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Record W2987952774 · doi:10.1002/jcb.29515

Comparison of the roles of estrogens and androgens in breast cancer and prostate cancer

2019· review· en· W2987952774 on OpenAlexafffund
Wenjing Liu, Gang Zhao, Chen‐Yan Zhang, Changqing Yang, Xiangbin Zeng, Jin Li, Kun Zhu, Shi‐Qi Zhao, Hui‐Meng Lu, Da‐Chuan Yin, Sheng‐Xiang Lin

Bibliographic record

VenueJournal of Cellular Biochemistry · 2019
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
FundersCanadian Institutes of Health ResearchChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsProstate cancerCancerBreast cancerProstateOncologyInternal medicineMedicineBiologyGynecology

Abstract

fetched live from OpenAlex

Breast cancer (BC) and prostate cancer (PC) are the second most common malignant tumors in women and men in western countries, respectively. The risks of death are 14% for BC and 9% for PC. Abnormal estrogen and androgen levels are related to carcinogenesis of the breast and prostate. Estradiol stimulates cancer development in BC. The effect of estrogen on PC is concentration-dependent, and estrogen can regulate androgen production, further affecting PC. Estrogen can also increase the risk of androgen-induced PC. Androgen has dual effects on BC via different metabolic pathways, and the role of the androgen receptor (AR) in BC also depends on cell subtype and downstream target genes. Androgen and AR can stimulate both primary PC and castration-resistant PC. Understanding the mechanisms of the effects of estrogen and androgen on BC and PC may help us to improve curative BC and PC treatment strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.382
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2019
Admission routes2
Has abstractyes

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